225 lines
11 KiB
Python
225 lines
11 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 The HuggingFace Inc. team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""
|
|
Image/Text processor class for OWL-ViT
|
|
"""
|
|
|
|
import warnings
|
|
from typing import List
|
|
|
|
import numpy as np
|
|
|
|
from ...processing_utils import ProcessorMixin
|
|
from ...tokenization_utils_base import BatchEncoding
|
|
from ...utils import is_flax_available, is_tf_available, is_torch_available
|
|
|
|
|
|
class OwlViTProcessor(ProcessorMixin):
|
|
r"""
|
|
Constructs an OWL-ViT processor which wraps [`OwlViTImageProcessor`] and [`CLIPTokenizer`]/[`CLIPTokenizerFast`]
|
|
into a single processor that interits both the image processor and tokenizer functionalities. See the
|
|
[`~OwlViTProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more information.
|
|
|
|
Args:
|
|
image_processor ([`OwlViTImageProcessor`], *optional*):
|
|
The image processor is a required input.
|
|
tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`], *optional*):
|
|
The tokenizer is a required input.
|
|
"""
|
|
|
|
attributes = ["image_processor", "tokenizer"]
|
|
image_processor_class = "OwlViTImageProcessor"
|
|
tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
|
|
|
|
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
|
|
feature_extractor = None
|
|
if "feature_extractor" in kwargs:
|
|
warnings.warn(
|
|
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
|
|
" instead.",
|
|
FutureWarning,
|
|
)
|
|
feature_extractor = kwargs.pop("feature_extractor")
|
|
|
|
image_processor = image_processor if image_processor is not None else feature_extractor
|
|
if image_processor is None:
|
|
raise ValueError("You need to specify an `image_processor`.")
|
|
if tokenizer is None:
|
|
raise ValueError("You need to specify a `tokenizer`.")
|
|
|
|
super().__init__(image_processor, tokenizer)
|
|
|
|
def __call__(self, text=None, images=None, query_images=None, padding="max_length", return_tensors="np", **kwargs):
|
|
"""
|
|
Main method to prepare for the model one or several text(s) and image(s). This method forwards the `text` and
|
|
`kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode:
|
|
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
|
|
CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
|
|
of the above two methods for more information.
|
|
|
|
Args:
|
|
text (`str`, `List[str]`, `List[List[str]]`):
|
|
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
|
|
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
|
|
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
|
|
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`,
|
|
`List[torch.Tensor]`):
|
|
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
|
|
tensor. Both channels-first and channels-last formats are supported.
|
|
query_images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
|
|
The query image to be prepared, one query image is expected per target image to be queried. Each image
|
|
can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image
|
|
should be of shape (C, H, W), where C is a number of channels, H and W are image height and width.
|
|
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
|
If set, will return tensors of a particular framework. Acceptable values are:
|
|
- `'tf'`: Return TensorFlow `tf.constant` objects.
|
|
- `'pt'`: Return PyTorch `torch.Tensor` objects.
|
|
- `'np'`: Return NumPy `np.ndarray` objects.
|
|
- `'jax'`: Return JAX `jnp.ndarray` objects.
|
|
Returns:
|
|
[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
|
|
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
|
|
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
|
|
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
|
|
`None`).
|
|
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
|
|
"""
|
|
|
|
if text is None and query_images is None and images is None:
|
|
raise ValueError(
|
|
"You have to specify at least one text or query image or image. All three cannot be none."
|
|
)
|
|
|
|
if text is not None:
|
|
if isinstance(text, str) or (isinstance(text, List) and not isinstance(text[0], List)):
|
|
encodings = [self.tokenizer(text, padding=padding, return_tensors=return_tensors, **kwargs)]
|
|
|
|
elif isinstance(text, List) and isinstance(text[0], List):
|
|
encodings = []
|
|
|
|
# Maximum number of queries across batch
|
|
max_num_queries = max([len(t) for t in text])
|
|
|
|
# Pad all batch samples to max number of text queries
|
|
for t in text:
|
|
if len(t) != max_num_queries:
|
|
t = t + [" "] * (max_num_queries - len(t))
|
|
|
|
encoding = self.tokenizer(t, padding=padding, return_tensors=return_tensors, **kwargs)
|
|
encodings.append(encoding)
|
|
else:
|
|
raise TypeError("Input text should be a string, a list of strings or a nested list of strings")
|
|
|
|
if return_tensors == "np":
|
|
input_ids = np.concatenate([encoding["input_ids"] for encoding in encodings], axis=0)
|
|
attention_mask = np.concatenate([encoding["attention_mask"] for encoding in encodings], axis=0)
|
|
|
|
elif return_tensors == "jax" and is_flax_available():
|
|
import jax.numpy as jnp
|
|
|
|
input_ids = jnp.concatenate([encoding["input_ids"] for encoding in encodings], axis=0)
|
|
attention_mask = jnp.concatenate([encoding["attention_mask"] for encoding in encodings], axis=0)
|
|
|
|
elif return_tensors == "pt" and is_torch_available():
|
|
import torch
|
|
|
|
input_ids = torch.cat([encoding["input_ids"] for encoding in encodings], dim=0)
|
|
attention_mask = torch.cat([encoding["attention_mask"] for encoding in encodings], dim=0)
|
|
|
|
elif return_tensors == "tf" and is_tf_available():
|
|
import tensorflow as tf
|
|
|
|
input_ids = tf.stack([encoding["input_ids"] for encoding in encodings], axis=0)
|
|
attention_mask = tf.stack([encoding["attention_mask"] for encoding in encodings], axis=0)
|
|
|
|
else:
|
|
raise ValueError("Target return tensor type could not be returned")
|
|
|
|
encoding = BatchEncoding()
|
|
encoding["input_ids"] = input_ids
|
|
encoding["attention_mask"] = attention_mask
|
|
|
|
if query_images is not None:
|
|
encoding = BatchEncoding()
|
|
query_pixel_values = self.image_processor(
|
|
query_images, return_tensors=return_tensors, **kwargs
|
|
).pixel_values
|
|
encoding["query_pixel_values"] = query_pixel_values
|
|
|
|
if images is not None:
|
|
image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs)
|
|
|
|
if text is not None and images is not None:
|
|
encoding["pixel_values"] = image_features.pixel_values
|
|
return encoding
|
|
elif query_images is not None and images is not None:
|
|
encoding["pixel_values"] = image_features.pixel_values
|
|
return encoding
|
|
elif text is not None or query_images is not None:
|
|
return encoding
|
|
else:
|
|
return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
|
|
|
|
def post_process(self, *args, **kwargs):
|
|
"""
|
|
This method forwards all its arguments to [`OwlViTImageProcessor.post_process`]. Please refer to the docstring
|
|
of this method for more information.
|
|
"""
|
|
return self.image_processor.post_process(*args, **kwargs)
|
|
|
|
def post_process_object_detection(self, *args, **kwargs):
|
|
"""
|
|
This method forwards all its arguments to [`OwlViTImageProcessor.post_process_object_detection`]. Please refer
|
|
to the docstring of this method for more information.
|
|
"""
|
|
return self.image_processor.post_process_object_detection(*args, **kwargs)
|
|
|
|
def post_process_image_guided_detection(self, *args, **kwargs):
|
|
"""
|
|
This method forwards all its arguments to [`OwlViTImageProcessor.post_process_one_shot_object_detection`].
|
|
Please refer to the docstring of this method for more information.
|
|
"""
|
|
return self.image_processor.post_process_image_guided_detection(*args, **kwargs)
|
|
|
|
def batch_decode(self, *args, **kwargs):
|
|
"""
|
|
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
|
refer to the docstring of this method for more information.
|
|
"""
|
|
return self.tokenizer.batch_decode(*args, **kwargs)
|
|
|
|
def decode(self, *args, **kwargs):
|
|
"""
|
|
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
|
the docstring of this method for more information.
|
|
"""
|
|
return self.tokenizer.decode(*args, **kwargs)
|
|
|
|
@property
|
|
def feature_extractor_class(self):
|
|
warnings.warn(
|
|
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
|
|
FutureWarning,
|
|
)
|
|
return self.image_processor_class
|
|
|
|
@property
|
|
def feature_extractor(self):
|
|
warnings.warn(
|
|
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
|
|
FutureWarning,
|
|
)
|
|
return self.image_processor
|